In view of the flaws of component-based software （CBS） reliability modeling and analysis, the low recognition degree of debugging process, too many assumptions and difficulties in obtaining the solution, a CBS relia...In view of the flaws of component-based software （CBS） reliability modeling and analysis, the low recognition degree of debugging process, too many assumptions and difficulties in obtaining the solution, a CBS reliability simulation process is presented incorporating the imperfect debugging and the limitation of debugging resources. Considering the effect of imperfect debugging on fault detec- tion and correction process, a CBS integration testing model is sketched by multi-queue muhichannel and finite server queuing model （MMFSQM）. Compared with the analytical method based on pa- rameters and other nonparametric approaches, the simulation approach can relax more of the usual reliability modeling assumptions and effectively expound integration testing process of CBS. Then, CBS reliability process simulation procedure is developed accordingly. The proposed simulation ap- proach is validated to be sound and effective by simulation experiment studies and analysis.展开更多
We present a novel system productivity simulation and optimization modeling framework in which equipment availability is a variable in the expected productivity function of the system. The framework is used for alloca...We present a novel system productivity simulation and optimization modeling framework in which equipment availability is a variable in the expected productivity function of the system. The framework is used for allocating trucks by route according to their operating performances in a truck-shovel system of an open-pit mine, so as to maximize the overall productivity of the fleet. We implement the framework in an originally designed and specifically developed simulator-optimizer software tool. We make an application on a real open-pit mine case study taking into account the stochasticity of the equipment behavior and environment. The total system production values obtained with and without considering the equipment reliability, availability and maintainability (RAM) characteristics are compared. We show that by taking into account the truck and shovel RAM aspects, we can maximize the total production of the system and obtain specific information on the production availability and productivity of its components.展开更多
The objective is to develop an approach for the determination of the target reliability index for serviceability limit state(SLS) of single piles. This contributes to conducting the SLS reliability-based design(RBD) o...The objective is to develop an approach for the determination of the target reliability index for serviceability limit state(SLS) of single piles. This contributes to conducting the SLS reliability-based design(RBD) of piles. Based on a two-parameter,hyperbolic curve-fitting equation describing the load-settlement relation of piles, the SLS model factor is defined. Then, taking into account the uncertainties of load-settlement model, load and bearing capacity of piles, the formula for computing the SLS reliability index(βsls) is obtained using the mean value first order second moment(MVFOSM) method. Meanwhile, the limit state function for conducting the SLS reliability analysis by the Monte Carlo simulation(MCS) method is established. These two methods are finally applied to determine the SLS target reliability index. Herein, the limiting tolerable settlement(slt) is treated as a random variable. For illustration, four load test databases from South Africa are compiled again to conduct reliability analysis and present the recommended target reliability indices. The results indicate that the MVFOSM method overestimates βsls compared to that computed by the MCS method. Besides, both factor of safety(FS) and slt are key factors influencing βsls, so the combination of FS and βsls is welcome to be used for the SLS reliability analysis of piles when slt is determined. For smaller slt, pile types and soils conditions have significant influence on the SLS target reliability indices; for larger slt, slt is the major factor having influence on the SLS target reliability indices. This proves that slt is the most key parameter for the determination of the SLS target reliability index.展开更多
This study builds a simulation of Chinese carbon sequestration market(CSM) based on the Swarm platform and complex adaptive system(CAS) theory.The simulation results represent that the total assets and profits of the ...This study builds a simulation of Chinese carbon sequestration market(CSM) based on the Swarm platform and complex adaptive system(CAS) theory.The simulation results represent that the total assets and profits of the carbon sequestration project(CSP) buyer and seller are steadily on the increase in the carbon trading maxket.The market regulatory efficiency is determined by the market investment and the improvement of regulation policy.Furthermore,the real sample simulation of Sichuan Daduhe Forest CSP demonstrates that the profit of CSP traded in the CSM is higher than the profit from the transactions of outside exchange.It implies that establishing CSM is an effective way to improve the CSP business for investors and a positive action to response to global warming as well.Finally,this study applies an Analytic Hierarchy Process-Fuzzy Comprehensive Evaluation(AHPFCE) approach to evaluate the reliability of CSM simulation.It concludes that the CSM simulation is "more creditable",which indicates that the CSM simulation results can be used as a proxy to observe the market uncertainties.展开更多
Simulation based structural reliability analysis suffers from a heavy computational burden, as each sample needs to be evaluated on the performance function, where structural analysis is performed. To alleviate the co...Simulation based structural reliability analysis suffers from a heavy computational burden, as each sample needs to be evaluated on the performance function, where structural analysis is performed. To alleviate the computational burden, related research focuses mainly on reduction of samples and application of surrogate model, which substitutes the performance function. However,the reduction of samples is achieved commonly at the expense of loss of robustness, and the construction of surrogate model is computationally expensive. In view of this, this paper presents a robust and efficient method in the same direction. The present method uses radial-based importance sampling (RBIS) to reduce samples without loss of robustness. Importantly, Kriging is fully used to efficiently implement RBIS. It not only serves as a surrogate to classify samples as we all know, but also guides the procedure to determine the optimal radius, with which RBIS would reduce samples to the highest degree. When used as a surrogate, Kriging is established through active learning, where the previously evaluated points to determine the optimal radius are reused. The robustness and efficiency of the present method are validated by five representative examples, where the present method is compared mainly with two fundamental reliability methods based on active learning Kriging.展开更多
Using 1200 CPUs of the National Supercomputer TH-A1 and a parallel integral algorithm based on the 3500th-order Taylor expansion and the 4180-digit multiple precision data,we have done a reliable simulation of chaotic...Using 1200 CPUs of the National Supercomputer TH-A1 and a parallel integral algorithm based on the 3500th-order Taylor expansion and the 4180-digit multiple precision data,we have done a reliable simulation of chaotic solution of Lorenz equation in a rather long interval 0 t 10000 LTU(Lorenz time unit).Such a kind of mathematically reliable chaotic simulation has never been reported.It provides us a numerical benchmark for mathematically reliable long-term prediction of chaos.Besides,it also proposes a safe method for mathematically reliable simulations of chaos in a finite but long enough interval.In addition,our very fine simulations suggest that such a kind of mathematically reliable long-term prediction of chaotic solution might have no physical meanings,because the inherent physical micro-level uncertainty due to thermal fluctuation might quickly transfer into macroscopic uncertainty so that trajectories for a long enough time would be essentially uncertain in physics.展开更多
According to Lorenz, chaotic dynamic systems have sensitive dependence on initial conditions（SDIC）, i.e., the butterfly-effect： a tiny difference on initial conditions might lead to huge difference of computer-gene...According to Lorenz, chaotic dynamic systems have sensitive dependence on initial conditions（SDIC）, i.e., the butterfly-effect： a tiny difference on initial conditions might lead to huge difference of computer-generated simulations after a long time. Thus, computer-generated chaotic results given by traditional algorithms in double precision are a kind of mixture of ＂true＂（convergent） solution and numerical noises at the same level. Today, this defect can be overcome by means of the ＂clean numerical simulation＂（CNS） with negligible numerical noises in a long enough interval of time. The CNS is based on the Taylor series method at high enough order and data in the multiple precision with large enough number of digits, plus a convergence check using an additional simulation with even smaller numerical noises. In theory, convergent（reliable） chaotic solutions can be obtained in an arbitrary long（but finite） interval of time by means of the CNS. The CNS can reduce numerical noises to such a level even much smaller than micro-level uncertainty of physical quantities that propagation of these physical micro-level uncertainties can be precisely investigated. In this paper, we briefly introduce the basic ideas of the CNS, and its applications in long-term reliable simulations of Lorenz equation, three-body problem and Rayleigh-Bénard turbulent flows. Using the CNS, it is found that a chaotic three-body system with symmetry might disrupt without any external disturbance, say, its symmetry-breaking and system-disruption are ＂self-excited＂, i.e., out-of-nothing. In addition, by means of the CNS, we can provide a rigorous theoretical evidence that the micro-level thermal fluctuation is the origin of macroscopic randomness of turbulent flows. Naturally, much more precise than traditional algorithms in double precision, the CNS can provide us a new way to more accurately investigate chaotic dynamic systems.展开更多
基金Supported by the National High Technology Research and Development Program of China(No.2008AA01A201)the National Nature Science Foundation of China(No.60503015,90818016)
文摘In view of the flaws of component-based software （CBS） reliability modeling and analysis, the low recognition degree of debugging process, too many assumptions and difficulties in obtaining the solution, a CBS reliability simulation process is presented incorporating the imperfect debugging and the limitation of debugging resources. Considering the effect of imperfect debugging on fault detec- tion and correction process, a CBS integration testing model is sketched by multi-queue muhichannel and finite server queuing model （MMFSQM）. Compared with the analytical method based on pa- rameters and other nonparametric approaches, the simulation approach can relax more of the usual reliability modeling assumptions and effectively expound integration testing process of CBS. Then, CBS reliability process simulation procedure is developed accordingly. The proposed simulation ap- proach is validated to be sound and effective by simulation experiment studies and analysis.
文摘We present a novel system productivity simulation and optimization modeling framework in which equipment availability is a variable in the expected productivity function of the system. The framework is used for allocating trucks by route according to their operating performances in a truck-shovel system of an open-pit mine, so as to maximize the overall productivity of the fleet. We implement the framework in an originally designed and specifically developed simulator-optimizer software tool. We make an application on a real open-pit mine case study taking into account the stochasticity of the equipment behavior and environment. The total system production values obtained with and without considering the equipment reliability, availability and maintainability (RAM) characteristics are compared. We show that by taking into account the truck and shovel RAM aspects, we can maximize the total production of the system and obtain specific information on the production availability and productivity of its components.
基金Projects(51278216,51308241)supported by the National Natural Science Foundation of ChinaProject(2013BS010)supported by the Funds of Henan University of Technology for High-level Talents,China
文摘The objective is to develop an approach for the determination of the target reliability index for serviceability limit state(SLS) of single piles. This contributes to conducting the SLS reliability-based design(RBD) of piles. Based on a two-parameter,hyperbolic curve-fitting equation describing the load-settlement relation of piles, the SLS model factor is defined. Then, taking into account the uncertainties of load-settlement model, load and bearing capacity of piles, the formula for computing the SLS reliability index(βsls) is obtained using the mean value first order second moment(MVFOSM) method. Meanwhile, the limit state function for conducting the SLS reliability analysis by the Monte Carlo simulation(MCS) method is established. These two methods are finally applied to determine the SLS target reliability index. Herein, the limiting tolerable settlement(slt) is treated as a random variable. For illustration, four load test databases from South Africa are compiled again to conduct reliability analysis and present the recommended target reliability indices. The results indicate that the MVFOSM method overestimates βsls compared to that computed by the MCS method. Besides, both factor of safety(FS) and slt are key factors influencing βsls, so the combination of FS and βsls is welcome to be used for the SLS reliability analysis of piles when slt is determined. For smaller slt, pile types and soils conditions have significant influence on the SLS target reliability indices; for larger slt, slt is the major factor having influence on the SLS target reliability indices. This proves that slt is the most key parameter for the determination of the SLS target reliability index.
基金supported by the National Natural Science Foundation of China under Grant No.71173175
文摘This study builds a simulation of Chinese carbon sequestration market(CSM) based on the Swarm platform and complex adaptive system(CAS) theory.The simulation results represent that the total assets and profits of the carbon sequestration project(CSP) buyer and seller are steadily on the increase in the carbon trading maxket.The market regulatory efficiency is determined by the market investment and the improvement of regulation policy.Furthermore,the real sample simulation of Sichuan Daduhe Forest CSP demonstrates that the profit of CSP traded in the CSM is higher than the profit from the transactions of outside exchange.It implies that establishing CSM is an effective way to improve the CSP business for investors and a positive action to response to global warming as well.Finally,this study applies an Analytic Hierarchy Process-Fuzzy Comprehensive Evaluation(AHPFCE) approach to evaluate the reliability of CSM simulation.It concludes that the CSM simulation is "more creditable",which indicates that the CSM simulation results can be used as a proxy to observe the market uncertainties.
基金supported by the National Natural Science Foundation of China (Grant No. 11421091)the Fundamental Research Funds for the Central Universities (Grant No. HIT.MKSTISP.2016 09)
文摘Simulation based structural reliability analysis suffers from a heavy computational burden, as each sample needs to be evaluated on the performance function, where structural analysis is performed. To alleviate the computational burden, related research focuses mainly on reduction of samples and application of surrogate model, which substitutes the performance function. However,the reduction of samples is achieved commonly at the expense of loss of robustness, and the construction of surrogate model is computationally expensive. In view of this, this paper presents a robust and efficient method in the same direction. The present method uses radial-based importance sampling (RBIS) to reduce samples without loss of robustness. Importantly, Kriging is fully used to efficiently implement RBIS. It not only serves as a surrogate to classify samples as we all know, but also guides the procedure to determine the optimal radius, with which RBIS would reduce samples to the highest degree. When used as a surrogate, Kriging is established through active learning, where the previously evaluated points to determine the optimal radius are reused. The robustness and efficiency of the present method are validated by five representative examples, where the present method is compared mainly with two fundamental reliability methods based on active learning Kriging.
基金partly supported by National Natural Science Foundation of China (Grant No. 11272209)National Basic Research Program of China (Grant No. 2011CB309704)State Key Laboratory of Ocean Engineering of China (Grant No. GKZD010056).
文摘Using 1200 CPUs of the National Supercomputer TH-A1 and a parallel integral algorithm based on the 3500th-order Taylor expansion and the 4180-digit multiple precision data,we have done a reliable simulation of chaotic solution of Lorenz equation in a rather long interval 0 t 10000 LTU(Lorenz time unit).Such a kind of mathematically reliable chaotic simulation has never been reported.It provides us a numerical benchmark for mathematically reliable long-term prediction of chaos.Besides,it also proposes a safe method for mathematically reliable simulations of chaos in a finite but long enough interval.In addition,our very fine simulations suggest that such a kind of mathematically reliable long-term prediction of chaotic solution might have no physical meanings,because the inherent physical micro-level uncertainty due to thermal fluctuation might quickly transfer into macroscopic uncertainty so that trajectories for a long enough time would be essentially uncertain in physics.
基金Project supported by the National Natural Science Foundation of China(Grant No.1432009)
文摘According to Lorenz, chaotic dynamic systems have sensitive dependence on initial conditions（SDIC）, i.e., the butterfly-effect： a tiny difference on initial conditions might lead to huge difference of computer-generated simulations after a long time. Thus, computer-generated chaotic results given by traditional algorithms in double precision are a kind of mixture of ＂true＂（convergent） solution and numerical noises at the same level. Today, this defect can be overcome by means of the ＂clean numerical simulation＂（CNS） with negligible numerical noises in a long enough interval of time. The CNS is based on the Taylor series method at high enough order and data in the multiple precision with large enough number of digits, plus a convergence check using an additional simulation with even smaller numerical noises. In theory, convergent（reliable） chaotic solutions can be obtained in an arbitrary long（but finite） interval of time by means of the CNS. The CNS can reduce numerical noises to such a level even much smaller than micro-level uncertainty of physical quantities that propagation of these physical micro-level uncertainties can be precisely investigated. In this paper, we briefly introduce the basic ideas of the CNS, and its applications in long-term reliable simulations of Lorenz equation, three-body problem and Rayleigh-Bénard turbulent flows. Using the CNS, it is found that a chaotic three-body system with symmetry might disrupt without any external disturbance, say, its symmetry-breaking and system-disruption are ＂self-excited＂, i.e., out-of-nothing. In addition, by means of the CNS, we can provide a rigorous theoretical evidence that the micro-level thermal fluctuation is the origin of macroscopic randomness of turbulent flows. Naturally, much more precise than traditional algorithms in double precision, the CNS can provide us a new way to more accurately investigate chaotic dynamic systems.
